Spec Miner

Automate and integrate Spec Miner to extract and analyze technical specifications efficiently

Category: productivity Source: Jeffallan/claude-skills

Spec Miner is an AI skill that extracts, organizes, and validates software specifications from various sources including documents, conversations, codebases, and issue trackers. It covers requirement extraction, specification formatting, consistency validation, gap detection, and traceability mapping that transform scattered information into structured, actionable specifications.

What Is This?

Overview

Spec Miner provides automated extraction and organization of software requirements from unstructured sources. It addresses requirement identification from meeting notes, emails, and chat conversations, specification structuring into standard formats with categorized functional and non-functional requirements, consistency checking that detects conflicting requirements across different sources, gap analysis that identifies missing specifications for common system concerns, traceability mapping that links requirements to implementation artifacts, and priority inference based on stakeholder language and context.

Who Should Use This

This skill serves business analysts compiling requirements from multiple stakeholders, product managers transforming feature requests into formal specifications, developers who need to extract implicit requirements from existing code, and project managers ensuring requirement coverage before development begins.

Why Use It?

Problems It Solves

Software requirements are scattered across documents, emails, meeting notes, and verbal conversations. Different stakeholders describe the same requirement using different terminology. Critical requirements are implied but never explicitly documented, leading to incomplete implementations. Manual specification gathering is time-consuming and prone to missed details.

Core Highlights

The skill parses natural language sources to identify requirement statements automatically. Extracted requirements are categorized by type: functional, performance, security, and usability. Conflict detection flags contradictory requirements from different stakeholders. Gap analysis checks for missing requirements in areas like error handling, authentication, and data validation.

How to Use It?

Basic Usage

class SpecMiner:
    def __init__(self):
        self.requirements = []
        self.sources = []

    def extract_from_document(self, document_text, source_name):
        sentences = self.split_into_sentences(document_text)
        for sentence in sentences:
            if self.is_requirement(sentence):
                req = {
                    "text": sentence,
                    "type": self.classify_requirement(sentence),
                    "source": source_name,
                    "priority": self.infer_priority(sentence),
                    "id": f"REQ-{len(self.requirements) + 1:04d}"
                }
                self.requirements.append(req)
        return self.requirements

    def classify_requirement(self, text):
        keywords = {
            "functional": ["shall", "must", "will allow", "should support"],
            "performance": ["latency", "throughput", "response time", "load"],
            "security": ["authentication", "encryption", "access control"],
            "usability": ["intuitive", "accessible", "user-friendly"]
        }
        for req_type, words in keywords.items():
            if any(w in text.lower() for w in words):
                return req_type
        return "functional"

Real-World Examples

def check_consistency(requirements):
    conflicts = []
    for i, req_a in enumerate(requirements):
        for req_b in requirements[i+1:]:
            similarity = compute_similarity(req_a["text"], req_b["text"])
            if similarity > 0.7 and req_a["source"] != req_b["source"]:
                if has_contradiction(req_a["text"], req_b["text"]):
                    conflicts.append({
                        "req_a": req_a["id"],
                        "req_b": req_b["id"],
                        "description": f"Potential conflict between sources"
                    })
    return conflicts

def analyze_gaps(requirements):
    expected_areas = ["authentication", "error_handling", "data_validation",
                      "logging", "backup", "rate_limiting"]
    covered = set()
    for req in requirements:
        for area in expected_areas:
            if area.replace("_", " ") in req["text"].lower():
                covered.add(area)
    gaps = [a for a in expected_areas if a not in covered]
    return {"covered": list(covered), "gaps": gaps,
            "coverage": f"{len(covered)}/{len(expected_areas)}"}

Advanced Tips

Create domain-specific keyword dictionaries that improve requirement classification accuracy for your industry. Use version control for specifications so changes can be tracked and reviewed like code. Implement bidirectional traceability linking requirements to tests so you can verify that every requirement has corresponding test coverage.

When to Use It?

Use Cases

Use Spec Miner when starting a project with requirements spread across multiple documents and conversations, when auditing existing specifications for completeness and consistency, when onboarding to a project and needing to understand what was specified, or when preparing for development sprints that need clear, prioritized requirements.

Important Notes

Requirements

Access to the source documents, conversations, or artifacts containing requirements. A classification taxonomy for organizing extracted requirements. Stakeholder availability for resolving identified conflicts and gaps.

Usage Recommendations

Do: validate extracted requirements with the original stakeholders to confirm accuracy. Assign unique identifiers to every requirement for traceability. Review gap analysis results with domain experts who can identify missing requirements the tool may not detect.

Don't: treat automatically extracted requirements as final without human review. Assume all requirements have equal priority without explicit stakeholder input. Skip consistency checking when requirements come from a single source, as internal contradictions are common.

Limitations

Automated extraction may misidentify descriptive statements as requirements or miss requirements phrased as questions or suggestions. Conflict detection works best for direct contradictions and may miss subtle inconsistencies in business logic. Domain-specific terminology may require custom configuration for accurate classification.